TY - GEN

T1 - Prediction of mechanical properties of Ti-6Al-4V using neural network

AU - Pratama, Detak Yan

AU - Junaidi, Syarif

AU - Ramli, R.

PY - 2010

Y1 - 2010

N2 - Objective of this study is to develop simulation for predicting mechanical properties of Ti-6Al-4V alloy. Rockwell Hardness (HRC), Ultimate tensile strength (UTS) and elongation (ε) are predicted by using Neural Network (NN) with multilayer feedforward architecture. The input of simulations are chemical compositions of Ti-alloy at room temperature. The data of the mechanical properties which are reported by other researchers are used for the NN training and Gradient Descent (GD) and Lavenberg Marquardt (LM) are applied as methods of learning algorithms. The results of training by both methods are compared in order to obtain high performance of output criteria which are determined by a Normalized Root Mean Square Error (NRMSE). is used to determine the performance of output criteria. In training, the NRMSE output calculated by GD algorithm show that HRC, UTS and ε are 0.024, 0.0717 and 0.1375 respectively, while LM algorithm for HRC, UTS and ε are 0.0207, 0.0689 and 0.1150, respectively. The NRMSE predicted output of GD algorithm for HRC, UTS, and ε are 0.0658, 0.0338 and 0.2994, while LM algorithm for HRC, UTS and ε are 0.0371, 0.1192 and 0.5487 respectively. In training, values of NRMSE calculated by LM algorithm is smaller than GD algorithm. These results suggest that LM algorithm shows excellent ability for training, however the GD method is more appropriate for the training algorithm in order to obtain a high performance of output criteria. It can be concluded that the NN can be applied for predicting mechanical properties of Ti-6Al-4V alloys.

AB - Objective of this study is to develop simulation for predicting mechanical properties of Ti-6Al-4V alloy. Rockwell Hardness (HRC), Ultimate tensile strength (UTS) and elongation (ε) are predicted by using Neural Network (NN) with multilayer feedforward architecture. The input of simulations are chemical compositions of Ti-alloy at room temperature. The data of the mechanical properties which are reported by other researchers are used for the NN training and Gradient Descent (GD) and Lavenberg Marquardt (LM) are applied as methods of learning algorithms. The results of training by both methods are compared in order to obtain high performance of output criteria which are determined by a Normalized Root Mean Square Error (NRMSE). is used to determine the performance of output criteria. In training, the NRMSE output calculated by GD algorithm show that HRC, UTS and ε are 0.024, 0.0717 and 0.1375 respectively, while LM algorithm for HRC, UTS and ε are 0.0207, 0.0689 and 0.1150, respectively. The NRMSE predicted output of GD algorithm for HRC, UTS, and ε are 0.0658, 0.0338 and 0.2994, while LM algorithm for HRC, UTS and ε are 0.0371, 0.1192 and 0.5487 respectively. In training, values of NRMSE calculated by LM algorithm is smaller than GD algorithm. These results suggest that LM algorithm shows excellent ability for training, however the GD method is more appropriate for the training algorithm in order to obtain a high performance of output criteria. It can be concluded that the NN can be applied for predicting mechanical properties of Ti-6Al-4V alloys.

KW - Learning algortihm

KW - Mechanical properties

KW - Neural network

KW - Prediction

KW - Ti-6Al-4V

UR - http://www.scopus.com/inward/record.url?scp=75749108578&partnerID=8YFLogxK

U2 - 10.4028/www.scientific.net/AMR.89-91.443

DO - 10.4028/www.scientific.net/AMR.89-91.443

M3 - Conference contribution

AN - SCOPUS:75749108578

SN - 0878492933

SN - 9780878492930

T3 - Advanced Materials Research

SP - 443

EP - 448

BT - THERMEC 2009 Supplement

T2 - 6th International Conference on Processing and Manufacturing of Advanced Materials - THERMEC'2009

Y2 - 25 August 2009 through 29 August 2009

ER -